Course Outline
Introduction to Deep Learning Explainability
- What are black-box models?
- The importance of transparency in AI systems.
- Overview of explainability challenges in neural networks.
Advanced XAI Techniques for Deep Learning
- Model-agnostic methods for deep learning: LIME, SHAP.
- Layer-wise relevance propagation (LRP).
- Saliency maps and gradient-based methods.
Explaining Neural Network Decisions
- Visualizing hidden layers in neural networks.
- Understanding attention mechanisms in deep learning models.
- Generating human-readable explanations from neural networks.
Tools for Explaining Deep Learning Models
- Introduction to open-source XAI libraries.
- Using Captum and InterpretML for deep learning.
- Integrating explainability techniques in TensorFlow and PyTorch.
Interpretability vs. Performance
- Trade-offs between accuracy and interpretability.
- Designing interpretable yet performant deep learning models.
- Handling bias and fairness in deep learning.
Real-World Applications of Deep Learning Explainability
- Explainability in healthcare AI models.
- Regulatory requirements for transparency in AI.
- Deploying interpretable deep learning models in production.
Ethical Considerations in Explainable Deep Learning
- Ethical implications of AI transparency.
- Balancing ethical AI practices with innovation.
- Privacy concerns in deep learning explainability.
Summary and Next Steps
Requirements
- Advanced understanding of deep learning.
- Familiarity with Python and deep learning frameworks.
- Practical experience working with neural networks.
Audience
- Deep learning engineers.
- AI specialists.
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
